Volume 8, Issue 1, February 2020, Page: 16-26
A Fault Detection Approach Using Variational Mode Decomposition, L-kurtosis and Random Decrement Technique for Rotating Machinery
Hui Liu, State Key Laboratory of Mechanics and Control of Mechanical Structures, Nanjing University of Aeronautics and Astronautics, Nanjing, China
Zhiyu Shi, State Key Laboratory of Mechanics and Control of Mechanical Structures, Nanjing University of Aeronautics and Astronautics, Nanjing, China
Received: Jan. 6, 2020;       Accepted: Jan. 21, 2020;       Published: Jan. 31, 2020
DOI: 10.11648/j.ijmea.20200801.13      View  230      Downloads  129
Abstract
Fault detection of rotating machinery under heavy noise background, is a significant but difficult issue, and traditional fault detection approaches are difficult to apply. To address this problem, a novel approach that combines variational mode decomposition (VMD), L-Kurtosis and random decrement technique (RDT) is proposed, which procedures are summarized as follows. First, the raw vibration signal collected from the rotating component is decomposed using VMD into a set of intrinsic mode functions (IMFs), and the noise components can be separated from the raw signal. Second, the L-Kurtosis indicator is introduced to solve the problem that the fault information is difficult to track, and the optimal intrinsic mode function (IMF) can be determined according to the maximum L-Kurtosis value. Then, RDT is further employed to purify the optimal IMF to eliminate the other unknown interference sources. Finally, a Hilbert envelope spectrum analysis is used for detecting the fault type. In order to validate the proposed approach, the numerical simulations and real experimental investigations about rolling element bearing and gear are conducted. The results illustrate that the proposed approach can effectively detect faults of rotating components.
Keywords
Rotating Machinery, Fault Detection, Variational Mode Decomposition, L-Kurtosis, Random Decrement Technique
To cite this article
Hui Liu, Zhiyu Shi, A Fault Detection Approach Using Variational Mode Decomposition, L-kurtosis and Random Decrement Technique for Rotating Machinery, International Journal of Mechanical Engineering and Applications. Vol. 8, No. 1, 2020, pp. 16-26. doi: 10.11648/j.ijmea.20200801.13
Copyright
Copyright © 2020 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Reference
[1]
Y. Zhang, and R. Randall, “Rolling element bearing fault diagnosis based on the combination of genetic algorithms and fast kurtogram,” Mech. Syst. Signal Pr., vol. 23, no. 5, pp. 1509-1517, July 2009.
[2]
M. Riera-Guasp, J. A. Antonino-Daviu, G. A. Capolino, “Advances in electrical machine, power electronic, and drive condition monitoring and fault detection: state of the art,” IEEE Trans. Ind. Electron., vol. 62, no. 3, pp. 1746-1759, March 2015.
[3]
Y. X. Wang, J. W. Xiang, R. Markert, and M. Liang, “Spectral kurtosis for fault detection, diagnosis and prognostics of rotating machines: a review with applications,” Mech. Syst. Signal Process., vol. 66-67, pp. 679-698, January 2016.
[4]
J. Liu, and Y. M. Shao, “Dynamic modeling for rigid rotor bearing systems with a localized defect considering additional deformations at the sharp edges,” J. Sound Vib., vol. 398, pp. 84-102, June 2017.
[5]
M. Cerrada, R. V. Sanchez, and C. Li, et al., “A review on data-driven fault severity assessment in rolling bearings,” Mech. Syst. Signal Process., vol. 99, pp. 169-196, January 2018.
[6]
J. Xiang, Y. Zhong, and H. Gao, “Rolling element bearing fault detection using PPCA and spectral kurtosis,” Measurement., vol. 75, pp. 180-191, November 2015.
[7]
S. L. Lu, P. Zheng, and Y. B. Liu, “Sound-aided vibration weak signal enhancement for bearing fault detection by using adaptive stochastic resonance,” j. Sound Vib., vol. 449, pp. 18-29, Juny 2019.
[8]
Z. W. Liu, Z. J. He, W. Guo, and Z. C. Tang, “A hybrid fault diagnosis method based on second generation wavelet de-noising and local mean decomposition for rotating machinery,” ISA T., vol. 61, pp. 211-220, March 2016.
[9]
H. Liu, and J. W. Xiang, “Kernel regression residual decomposition-based synchroextracting transform to detect faults in mechanical systems,” ISA T., vol. 87, pp. 251-263, April 2019.
[10]
H. M. Zhao, H. L. Liu, and J. J. et al., “Research on a fault diagnosis method of rolling bearings using variation mode decomposition and deep belief network,” J. Mech. Sci. Technol., vol. 33, no. 9, pp. 4165-4172, September 2019.
[11]
K. X. Hong, and G. X. Lin, “State classification of transformers using nonlinear dynamic analysis and Hidden Markov models,” Measurement, vol. 147, Dec 2019.
[12]
J. Y. Jiao, M. Zhao, J. Lin, Ding, and C. C. Ding, “Deep coupled dense convolutional network with complementary data for intelligent fault diagnosis,” IEEE T. Ind. Electron., vol. 66, no. 12, pp. 9858-9867, December 2019.
[13]
J. W. Xiang, and Y. T. Zhong, “A fault detection strategy using the enhancement ensemble empirical mode decomposition and random decrement technique,” Microelectronics Reliab., vol. 75, pp. 317-326, August 2017.
[14]
N. E. Huang, Z. Shen, and S. R. Long, et al., “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis,” Proc. Royal Soc. A: Math., Phys. Eng. Sci., vol. 454, no. 1971, pp. 903-995, March 1998.
[15]
K. Dragomiretskiy, and D. Zosso, ‘‘Variational mode decomposition,” IEEE Trans. Signal Process., vol. 62, no. 3, pp. 531-544, February 2014.
[16]
X. Zheng, Q. Zhou, N. Zhou, R. J. Liu, Z. Y. Hao, and Y. Qiu, ‘‘A dichotomy-based variational mode decomposition method for rotating machinery fault diagnosis,” Meas. Sci. Technol., vol. 31, no. 1, pp. 015003, January 2019.
[17]
R. Gu, J. Chen, R. J. Hong, H. Wang, and W. W. Wu, ‘‘Incipient fault diagnosis of rolling bearings based on adaptive variational mode decomposition and Teager energy operator,” Measurement, vol. 149, January 2019.
[18]
X. W. Du, G. R. Wen, and D. Liu, et al., ‘‘Fractional iterative variational mode decomposition and its application in fault diagnosis of rotating machinery,” Meas. Sci. Technol., vol. 30, no. 12, December 2019.
[19]
C. Yang, and M. P. Jia, ‘‘A novel weak fault signal detection approach for a rolling bearing using variational mode decomposition and phase space parallel factor analysis,” Meas. Sci. Technol., vol. 30, no. 11, November 2019.
[20]
C. S. Withers, and S. Nadarajah, ‘‘Bias-reduced estimates for skewness, kurtosis, L-skewness and L-kurtosis,” J. Stat. Plan. Infer., vol. 141, no. 12, pp. 3839-3861, December 2011.
[21]
S. P. Liu, S. M. Hou, K. D. He, and W. H. Yang, ‘‘L-Kurtosis and its application for fault detection of rolling element bearings,” Measurement, vol. 116, pp. 523-532, February 2018.
[22]
H. A. Cole, ‘‘On-the-line analysis of random vibrations,” AIAA Pap., vol. 68, pp. 288-319, 1971.
[23]
M. J. Desforges, J. E. Cooper, and J. R. Wright, ‘‘Spectral and modal parameter estimation from output-only measurements,” Mech. Syst. Signal Pr., vol. 9 no. 2, pp. 169-186, March 1995.
[24]
S. R. Ibrahim, K. R. Wentx, and J. Lee, ‘‘Damping identification from non-linear random response using a multi-triggering random decrement technique,” Mech. Syst. Signal Pr., vol. 1, no. 4, pp. 389-397, October 1987.
[25]
S. V. Modak, C. Rawal, and T. K. Kundra, ‘‘Harmonics elimination algorithm for operational modal analysis using random decrement technique,” Mech. Syst. Signal Pr., vol. 24, no. 4, pp. 922-944, May 2010.
[26]
J. C. Asmussen, R. Brincker, and S. R. Ibrahim, ‘‘Statistical theory of the vector random decrement technique,” J. Sound Vib., vol. 226, no. 2, pp. 329-344, September 1999.
[27]
J. K. Vandiver, A. B. Dunwoody, and R. B. Campbell, ‘‘A mathematical basis for the random decrement vibration signature analysis technique,” J. Mech. Design., vol. 104, pp. 307-313, 1982.
[28]
Z. W. Huang, Y. Z. Li, X. G. Hua, Z. Q. Chen, and Q. Wen, ‘‘Automatic Identification of Bridge Vortex-Induced Vibration Using Random Decrement Method,” Appl. Sci-basel., vol. 9, no. 10, May 2019.
[29]
S. R. Ibrahim, ‘‘The use of random decrement technique for identification of structural modes of vibration,”, AIAA Pap., vol. 77, pp. 1-9, 1977.
[30]
S. R. Ibrahim, “Random decrement technique for modal identification of structures,” J. Spacecr. Rocket., vol. 14, pp. 696-700, 1977.
[31]
A. N. Tichonov, “Solution of incorrectly formulated problems and the regularization method,” Soviet Math., vol. 4, pp. 1035-1038, 1963.
[32]
D. P. Bertsekas, “Multiplier methods: A survey,” Automatica., vol. 12, no. 2, pp. 133-145, 1976.
[33]
L. L. Cui, and Y. Zhang, et al., “Application of pattern recognition in gear faults based on the matching pursuit of a characteristic waveform,” Measurement, vol. 104, pp. 212-222, July 2017.
Browse journals by subject